{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 导包"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-20T09:37:12.118743Z",
     "start_time": "2020-04-20T09:37:06.477613Z"
    }
   },
   "outputs": [],
   "source": [
    "import pathlib\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "import tensorflow as tf\n",
    "\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 下载数据集"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "数据来源kaggle，[官网地址](https://www.kaggle.com/c/boston-housing)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-20T09:38:46.961019Z",
     "start_time": "2020-04-20T09:38:45.273142Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://s3.amazonaws.com/keras-datasets/boston_housing.npz\n",
      "57344/57026 [==============================] - 1s 9us/step\n",
      "(404, 13)\n",
      "(102, 13)\n"
     ]
    }
   ],
   "source": [
    "from keras.datasets import boston_housing\n",
    "\n",
    "(train_data,train_targets),(test_data,test_targets) = boston_housing.load_data()\n",
    "#训练集形状：\n",
    "print(train_data.shape)\n",
    "#测试集形状:\n",
    "print(test_data.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 使用Pandas处理数据"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "共14个输入特征，分别为：\n",
    "\n",
    "crim:犯罪率、zn:城镇住宅用地占比、indus:城镇非商业用地占比、chas：查尔斯指数\n",
    "\n",
    "nox：环保指标、rm：房间数、age：房龄比例、dis：与就业中心的距离\n",
    "\n",
    "rad：高度路便利指数、tax：税率、ptratio：城镇师生比、black：黑人比例\n",
    "\n",
    "lstat：房东是收入阶层的比例、medv：房价中位数(目标，train_targets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-20T09:56:12.222240Z",
     "start_time": "2020-04-20T09:56:12.219039Z"
    }
   },
   "outputs": [],
   "source": [
    "column_names = ['crim', 'zn', 'indus', 'chas', 'nox',\n",
    "                'rm', 'age', 'dis', 'rad', 'tax', 'ptratio', 'black',\n",
    "                'lstat']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-20T11:45:18.196429Z",
     "start_time": "2020-04-20T11:45:18.172761Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>crim</th>\n",
       "      <th>zn</th>\n",
       "      <th>indus</th>\n",
       "      <th>chas</th>\n",
       "      <th>nox</th>\n",
       "      <th>rm</th>\n",
       "      <th>age</th>\n",
       "      <th>dis</th>\n",
       "      <th>rad</th>\n",
       "      <th>tax</th>\n",
       "      <th>ptratio</th>\n",
       "      <th>black</th>\n",
       "      <th>lstat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.23247</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.14</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5380</td>\n",
       "      <td>6.142</td>\n",
       "      <td>91.7</td>\n",
       "      <td>3.9769</td>\n",
       "      <td>4.0</td>\n",
       "      <td>307.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>396.90</td>\n",
       "      <td>18.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.02177</td>\n",
       "      <td>82.5</td>\n",
       "      <td>2.03</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.4150</td>\n",
       "      <td>7.610</td>\n",
       "      <td>15.7</td>\n",
       "      <td>6.2700</td>\n",
       "      <td>2.0</td>\n",
       "      <td>348.0</td>\n",
       "      <td>14.7</td>\n",
       "      <td>395.38</td>\n",
       "      <td>3.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.89822</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.6310</td>\n",
       "      <td>4.970</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1.3325</td>\n",
       "      <td>24.0</td>\n",
       "      <td>666.0</td>\n",
       "      <td>20.2</td>\n",
       "      <td>375.52</td>\n",
       "      <td>3.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.03961</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.19</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5150</td>\n",
       "      <td>6.037</td>\n",
       "      <td>34.5</td>\n",
       "      <td>5.9853</td>\n",
       "      <td>5.0</td>\n",
       "      <td>224.0</td>\n",
       "      <td>20.2</td>\n",
       "      <td>396.90</td>\n",
       "      <td>8.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.69311</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.7130</td>\n",
       "      <td>6.376</td>\n",
       "      <td>88.4</td>\n",
       "      <td>2.5671</td>\n",
       "      <td>24.0</td>\n",
       "      <td>666.0</td>\n",
       "      <td>20.2</td>\n",
       "      <td>391.43</td>\n",
       "      <td>14.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399</th>\n",
       "      <td>0.21977</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.91</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.4480</td>\n",
       "      <td>5.602</td>\n",
       "      <td>62.0</td>\n",
       "      <td>6.0877</td>\n",
       "      <td>3.0</td>\n",
       "      <td>233.0</td>\n",
       "      <td>17.9</td>\n",
       "      <td>396.90</td>\n",
       "      <td>16.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>400</th>\n",
       "      <td>0.16211</td>\n",
       "      <td>20.0</td>\n",
       "      <td>6.96</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.4640</td>\n",
       "      <td>6.240</td>\n",
       "      <td>16.3</td>\n",
       "      <td>4.4290</td>\n",
       "      <td>3.0</td>\n",
       "      <td>223.0</td>\n",
       "      <td>18.6</td>\n",
       "      <td>396.90</td>\n",
       "      <td>6.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>401</th>\n",
       "      <td>0.03466</td>\n",
       "      <td>35.0</td>\n",
       "      <td>6.06</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.4379</td>\n",
       "      <td>6.031</td>\n",
       "      <td>23.3</td>\n",
       "      <td>6.6407</td>\n",
       "      <td>1.0</td>\n",
       "      <td>304.0</td>\n",
       "      <td>16.9</td>\n",
       "      <td>362.25</td>\n",
       "      <td>7.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>402</th>\n",
       "      <td>2.14918</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19.58</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.8710</td>\n",
       "      <td>5.709</td>\n",
       "      <td>98.5</td>\n",
       "      <td>1.6232</td>\n",
       "      <td>5.0</td>\n",
       "      <td>403.0</td>\n",
       "      <td>14.7</td>\n",
       "      <td>261.95</td>\n",
       "      <td>15.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>403</th>\n",
       "      <td>0.01439</td>\n",
       "      <td>60.0</td>\n",
       "      <td>2.93</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.4010</td>\n",
       "      <td>6.604</td>\n",
       "      <td>18.8</td>\n",
       "      <td>6.2196</td>\n",
       "      <td>1.0</td>\n",
       "      <td>265.0</td>\n",
       "      <td>15.6</td>\n",
       "      <td>376.70</td>\n",
       "      <td>4.38</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>404 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        crim    zn  indus  chas     nox     rm    age     dis   rad    tax  \\\n",
       "0    1.23247   0.0   8.14   0.0  0.5380  6.142   91.7  3.9769   4.0  307.0   \n",
       "1    0.02177  82.5   2.03   0.0  0.4150  7.610   15.7  6.2700   2.0  348.0   \n",
       "2    4.89822   0.0  18.10   0.0  0.6310  4.970  100.0  1.3325  24.0  666.0   \n",
       "3    0.03961   0.0   5.19   0.0  0.5150  6.037   34.5  5.9853   5.0  224.0   \n",
       "4    3.69311   0.0  18.10   0.0  0.7130  6.376   88.4  2.5671  24.0  666.0   \n",
       "..       ...   ...    ...   ...     ...    ...    ...     ...   ...    ...   \n",
       "399  0.21977   0.0   6.91   0.0  0.4480  5.602   62.0  6.0877   3.0  233.0   \n",
       "400  0.16211  20.0   6.96   0.0  0.4640  6.240   16.3  4.4290   3.0  223.0   \n",
       "401  0.03466  35.0   6.06   0.0  0.4379  6.031   23.3  6.6407   1.0  304.0   \n",
       "402  2.14918   0.0  19.58   0.0  0.8710  5.709   98.5  1.6232   5.0  403.0   \n",
       "403  0.01439  60.0   2.93   0.0  0.4010  6.604   18.8  6.2196   1.0  265.0   \n",
       "\n",
       "     ptratio   black  lstat  \n",
       "0       21.0  396.90  18.72  \n",
       "1       14.7  395.38   3.11  \n",
       "2       20.2  375.52   3.26  \n",
       "3       20.2  396.90   8.01  \n",
       "4       20.2  391.43  14.65  \n",
       "..       ...     ...    ...  \n",
       "399     17.9  396.90  16.20  \n",
       "400     18.6  396.90   6.59  \n",
       "401     16.9  362.25   7.83  \n",
       "402     14.7  261.95  15.79  \n",
       "403     15.6  376.70   4.38  \n",
       "\n",
       "[404 rows x 13 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_train_data = pd.DataFrame(train_data)\n",
    "raw_train_data.columns = column_names\n",
    "\n",
    "raw_train_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-20T11:46:00.432654Z",
     "start_time": "2020-04-20T11:46:00.422907Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>medv</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>15.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>42.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>21.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399</th>\n",
       "      <td>19.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>400</th>\n",
       "      <td>25.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>401</th>\n",
       "      <td>19.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>402</th>\n",
       "      <td>19.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>403</th>\n",
       "      <td>29.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>404 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     medv\n",
       "0    15.2\n",
       "1    42.3\n",
       "2    50.0\n",
       "3    21.1\n",
       "4    17.7\n",
       "..    ...\n",
       "399  19.4\n",
       "400  25.2\n",
       "401  19.4\n",
       "402  19.4\n",
       "403  29.1\n",
       "\n",
       "[404 rows x 1 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_train_targets = pd.DataFrame(train_targets)\n",
    "raw_train_targets.columns = ['medv']\n",
    "\n",
    "raw_train_targets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-20T11:49:30.354123Z",
     "start_time": "2020-04-20T11:49:30.351714Z"
    }
   },
   "outputs": [],
   "source": [
    "train_data_pd = raw_train_data\n",
    "train_targets_pd = raw_train_targets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据检查"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 使用seaborn绘制联合分布\n",
    "检视数据分布的正确性与数据间的基本关系，**可获取显著信息**\n",
    "\n",
    "对角线图像为数值出现频率，其余对应位置为两数据间的关系。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "此处检查犯罪率、房龄、就业中心距离与房价中位数的关系\n",
    "\n",
    "可见低犯罪率区域较多，房龄越大的区域犯罪率越高"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-20T16:25:12.598685Z",
     "start_time": "2020-04-20T16:25:08.549542Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(pd.concat([train_data_pd[['crim', 'age', 'dis']],\n",
    "                        train_targets_pd], axis=1), diag_kind=\"kde\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 查看总体数据统计"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "通过`describe()`查看常用统计量，便于数据分析及后续归一化操作"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-20T16:26:43.993740Z",
     "start_time": "2020-04-20T16:26:43.944940Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>crim</th>\n",
       "      <td>404.0</td>\n",
       "      <td>3.745111</td>\n",
       "      <td>9.240734</td>\n",
       "      <td>0.00632</td>\n",
       "      <td>0.081437</td>\n",
       "      <td>0.26888</td>\n",
       "      <td>3.674808</td>\n",
       "      <td>88.9762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zn</th>\n",
       "      <td>404.0</td>\n",
       "      <td>11.480198</td>\n",
       "      <td>23.767711</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>12.500000</td>\n",
       "      <td>100.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>indus</th>\n",
       "      <td>404.0</td>\n",
       "      <td>11.104431</td>\n",
       "      <td>6.811308</td>\n",
       "      <td>0.46000</td>\n",
       "      <td>5.130000</td>\n",
       "      <td>9.69000</td>\n",
       "      <td>18.100000</td>\n",
       "      <td>27.7400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chas</th>\n",
       "      <td>404.0</td>\n",
       "      <td>0.061881</td>\n",
       "      <td>0.241238</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nox</th>\n",
       "      <td>404.0</td>\n",
       "      <td>0.557356</td>\n",
       "      <td>0.117293</td>\n",
       "      <td>0.38500</td>\n",
       "      <td>0.453000</td>\n",
       "      <td>0.53800</td>\n",
       "      <td>0.631000</td>\n",
       "      <td>0.8710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rm</th>\n",
       "      <td>404.0</td>\n",
       "      <td>6.267082</td>\n",
       "      <td>0.709788</td>\n",
       "      <td>3.56100</td>\n",
       "      <td>5.874750</td>\n",
       "      <td>6.19850</td>\n",
       "      <td>6.609000</td>\n",
       "      <td>8.7250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>404.0</td>\n",
       "      <td>69.010644</td>\n",
       "      <td>27.940665</td>\n",
       "      <td>2.90000</td>\n",
       "      <td>45.475000</td>\n",
       "      <td>78.50000</td>\n",
       "      <td>94.100000</td>\n",
       "      <td>100.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dis</th>\n",
       "      <td>404.0</td>\n",
       "      <td>3.740271</td>\n",
       "      <td>2.030215</td>\n",
       "      <td>1.12960</td>\n",
       "      <td>2.077100</td>\n",
       "      <td>3.14230</td>\n",
       "      <td>5.118000</td>\n",
       "      <td>10.7103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rad</th>\n",
       "      <td>404.0</td>\n",
       "      <td>9.440594</td>\n",
       "      <td>8.698360</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.00000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>24.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tax</th>\n",
       "      <td>404.0</td>\n",
       "      <td>405.898515</td>\n",
       "      <td>166.374543</td>\n",
       "      <td>188.00000</td>\n",
       "      <td>279.000000</td>\n",
       "      <td>330.00000</td>\n",
       "      <td>666.000000</td>\n",
       "      <td>711.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ptratio</th>\n",
       "      <td>404.0</td>\n",
       "      <td>18.475990</td>\n",
       "      <td>2.200382</td>\n",
       "      <td>12.60000</td>\n",
       "      <td>17.225000</td>\n",
       "      <td>19.10000</td>\n",
       "      <td>20.200000</td>\n",
       "      <td>22.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>black</th>\n",
       "      <td>404.0</td>\n",
       "      <td>354.783168</td>\n",
       "      <td>94.111148</td>\n",
       "      <td>0.32000</td>\n",
       "      <td>374.672500</td>\n",
       "      <td>391.25000</td>\n",
       "      <td>396.157500</td>\n",
       "      <td>396.9000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lstat</th>\n",
       "      <td>404.0</td>\n",
       "      <td>12.740817</td>\n",
       "      <td>7.254545</td>\n",
       "      <td>1.73000</td>\n",
       "      <td>6.890000</td>\n",
       "      <td>11.39500</td>\n",
       "      <td>17.092500</td>\n",
       "      <td>37.9700</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         count        mean         std        min         25%        50%  \\\n",
       "crim     404.0    3.745111    9.240734    0.00632    0.081437    0.26888   \n",
       "zn       404.0   11.480198   23.767711    0.00000    0.000000    0.00000   \n",
       "indus    404.0   11.104431    6.811308    0.46000    5.130000    9.69000   \n",
       "chas     404.0    0.061881    0.241238    0.00000    0.000000    0.00000   \n",
       "nox      404.0    0.557356    0.117293    0.38500    0.453000    0.53800   \n",
       "rm       404.0    6.267082    0.709788    3.56100    5.874750    6.19850   \n",
       "age      404.0   69.010644   27.940665    2.90000   45.475000   78.50000   \n",
       "dis      404.0    3.740271    2.030215    1.12960    2.077100    3.14230   \n",
       "rad      404.0    9.440594    8.698360    1.00000    4.000000    5.00000   \n",
       "tax      404.0  405.898515  166.374543  188.00000  279.000000  330.00000   \n",
       "ptratio  404.0   18.475990    2.200382   12.60000   17.225000   19.10000   \n",
       "black    404.0  354.783168   94.111148    0.32000  374.672500  391.25000   \n",
       "lstat    404.0   12.740817    7.254545    1.73000    6.890000   11.39500   \n",
       "\n",
       "                75%       max  \n",
       "crim       3.674808   88.9762  \n",
       "zn        12.500000  100.0000  \n",
       "indus     18.100000   27.7400  \n",
       "chas       0.000000    1.0000  \n",
       "nox        0.631000    0.8710  \n",
       "rm         6.609000    8.7250  \n",
       "age       94.100000  100.0000  \n",
       "dis        5.118000   10.7103  \n",
       "rad       24.000000   24.0000  \n",
       "tax      666.000000  711.0000  \n",
       "ptratio   20.200000   22.0000  \n",
       "black    396.157500  396.9000  \n",
       "lstat     17.092500   37.9700  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_stats = train_data_pd.describe()\n",
    "train_stats = train_stats.transpose()\n",
    "train_stats"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据预处理：标准化"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "pands数据与numpy数据有不同的标准化方式"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-20T16:39:43.876659Z",
     "start_time": "2020-04-20T16:39:43.862139Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def std_pd(x):\n",
    "    return (x - train_stats['mean']) / train_stats['std']\n",
    "\n",
    "\n",
    "def std_np(x):\n",
    "    mu = np.mean(x, axis=0)\n",
    "    sigma = np.std(x, axis=0)\n",
    "    return (x-mu)/sigma\n",
    "\n",
    "\n",
    "normed_train_data = std_pd(train_data_pd)\n",
    "normed_test_data = std_np(test_data)  # 对测试集也进行相同的归一化"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-20T16:38:10.394887Z",
     "start_time": "2020-04-20T16:38:10.378326Z"
    }
   },
   "source": [
    "## 构建模型"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "使用tensorflow的`keras.Sequential`构建模型\n",
    "- 使用MSE均方误差（L2范数损失），作为训练指标\n",
    "\n",
    "$$\\frac{1}{m}\\sum_{m}^{i=1}\\left (y _{i}-\\hat{y} _{i} \\right )^{2}$$\n",
    "\n",
    "- 使用MAE平均绝对误差（L1范数损失），作为评价指标\n",
    "\n",
    "$$\\frac{1}{m}\\sum_{m}^{i=1}\\left |y _{i}-\\hat{y}_{i}\\right |$$\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-20T16:59:12.615237Z",
     "start_time": "2020-04-20T16:59:12.504411Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_3 (Dense)              (None, 64)                896       \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 64)                4160      \n",
      "_________________________________________________________________\n",
      "dense_5 (Dense)              (None, 1)                 65        \n",
      "=================================================================\n",
      "Total params: 5,121\n",
      "Trainable params: 5,121\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "def build_model():\n",
    "    model = keras.Sequential([\n",
    "        layers.Dense(64, activation='relu', input_shape=[len(train_data_pd.keys())]),\n",
    "        layers.Dense(64, activation='relu'),\n",
    "        layers.Dense(1)\n",
    "    ])\n",
    "\n",
    "    optimizer = tf.keras.optimizers.RMSprop(0.001)\n",
    "\n",
    "    model.compile(loss='mse',\n",
    "                  optimizer=optimizer,\n",
    "                  metrics=['mae', 'mse'])\n",
    "    return model\n",
    "\n",
    "\n",
    "model = build_model()\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 训练模型"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 小批量试验"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-20T16:59:14.919901Z",
     "start_time": "2020-04-20T16:59:14.761722Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.19680536],\n",
       "       [ 0.01068957],\n",
       "       [-0.0177602 ],\n",
       "       [ 0.07004069],\n",
       "       [-0.0952743 ],\n",
       "       [-0.05520313],\n",
       "       [ 0.5763115 ],\n",
       "       [ 0.29303262],\n",
       "       [ 0.11095768],\n",
       "       [ 0.07484732]], dtype=float32)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "example_batch = normed_test_data[:10]  # 取10条数据做小规模试验\n",
    "example_result = model.predict(example_batch)\n",
    "example_result  # 得到预期的形状和结果类型"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 正式训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-20T16:59:46.663760Z",
     "start_time": "2020-04-20T16:59:17.487796Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "....................................................................................................\n",
      "....................................................................................................\n",
      "....................................................................................................\n",
      "....................................................................................................\n",
      "....................................................................................................\n",
      "....................................................................................................\n",
      "....................................................................................................\n",
      "....................................................................................................\n",
      "....................................................................................................\n",
      "...................................................................................................."
     ]
    }
   ],
   "source": [
    "# 训练伦次较多，自定义回调函数，代替进度条\n",
    "class PrintDot(keras.callbacks.Callback):\n",
    "    def on_epoch_end(self, epoch, logs):\n",
    "        if epoch % 100 == 0:\n",
    "            print('')\n",
    "        print('.', end='')\n",
    "\n",
    "EPOCH = 1000\n",
    "# 使用history对象存储训练进度\n",
    "history = model.fit(\n",
    "    normed_train_data, train_targets_pd,\n",
    "    epochs=EPOCH, validation_split=0.2, verbose=0,\n",
    "    callbacks=[PrintDot()]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 检查训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-20T17:00:16.856958Z",
     "start_time": "2020-04-20T17:00:16.841276Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>loss</th>\n",
       "      <th>mean_absolute_error</th>\n",
       "      <th>mean_squared_error</th>\n",
       "      <th>val_loss</th>\n",
       "      <th>val_mean_absolute_error</th>\n",
       "      <th>val_mean_squared_error</th>\n",
       "      <th>epoch</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>0.465701</td>\n",
       "      <td>0.459712</td>\n",
       "      <td>0.465701</td>\n",
       "      <td>23.147124</td>\n",
       "      <td>2.703835</td>\n",
       "      <td>23.147123</td>\n",
       "      <td>995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>0.638350</td>\n",
       "      <td>0.552443</td>\n",
       "      <td>0.638350</td>\n",
       "      <td>23.876562</td>\n",
       "      <td>2.712860</td>\n",
       "      <td>23.876562</td>\n",
       "      <td>996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>0.482335</td>\n",
       "      <td>0.479782</td>\n",
       "      <td>0.482335</td>\n",
       "      <td>22.532503</td>\n",
       "      <td>2.795424</td>\n",
       "      <td>22.532503</td>\n",
       "      <td>997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>0.679401</td>\n",
       "      <td>0.565874</td>\n",
       "      <td>0.679401</td>\n",
       "      <td>24.245610</td>\n",
       "      <td>2.773400</td>\n",
       "      <td>24.245609</td>\n",
       "      <td>998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>0.566069</td>\n",
       "      <td>0.511858</td>\n",
       "      <td>0.566069</td>\n",
       "      <td>23.807005</td>\n",
       "      <td>2.772023</td>\n",
       "      <td>23.807005</td>\n",
       "      <td>999</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         loss  mean_absolute_error  mean_squared_error   val_loss  \\\n",
       "995  0.465701             0.459712            0.465701  23.147124   \n",
       "996  0.638350             0.552443            0.638350  23.876562   \n",
       "997  0.482335             0.479782            0.482335  22.532503   \n",
       "998  0.679401             0.565874            0.679401  24.245610   \n",
       "999  0.566069             0.511858            0.566069  23.807005   \n",
       "\n",
       "     val_mean_absolute_error  val_mean_squared_error  epoch  \n",
       "995                 2.703835               23.147123    995  \n",
       "996                 2.712860               23.876562    996  \n",
       "997                 2.795424               22.532503    997  \n",
       "998                 2.773400               24.245609    998  \n",
       "999                 2.772023               23.807005    999  "
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hist = pd.DataFrame(history.history)\n",
    "hist['epoch'] = history.epoch\n",
    "hist.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 训练数据可视化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-20T17:14:13.132021Z",
     "start_time": "2020-04-20T17:14:12.742096Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_history(history):\n",
    "    hist = pd.DataFrame(history.history)\n",
    "    hist['epoch'] = history.epoch\n",
    "\n",
    "    # 绘制训练集与测试集的平均绝对误差图像\n",
    "    plt.figure()\n",
    "    plt.xlabel('Epoch')\n",
    "    plt.ylabel('Mean Abs Error [medv]')\n",
    "    plt.plot(hist['epoch'], hist['mean_absolute_error'],\n",
    "             label='Train Error')  # 以批次为横坐标，mae为纵坐标，并制定图例名称\n",
    "    plt.plot(hist['epoch'], hist['val_mean_absolute_error'],\n",
    "             label='Val Error')\n",
    "    plt.ylim([0, 5])\n",
    "    plt.legend()  # 加上图例\n",
    "\n",
    "    # 绘制训练集与测试集的均方误差图像\n",
    "    plt.figure()\n",
    "    plt.xlabel('Epoch')\n",
    "    plt.ylabel('Mean Square Error [$medv^2$]')\n",
    "    plt.plot(hist['epoch'], hist['mean_squared_error'],\n",
    "             label='Train Error')\n",
    "    plt.plot(hist['epoch'], hist['val_mean_squared_error'],\n",
    "             label='Val Error')\n",
    "    plt.ylim([0, 40])\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "# %%\n",
    "plot_history(history)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以看出在训练到100轮左右时，验证集误差出现了反弹，猜测是产生了过拟合"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 验证模型"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 使用测试集验证模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-20T17:15:54.035653Z",
     "start_time": "2020-04-20T17:15:54.012096Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "102/102 - 0s - loss: 25.8886 - mean_absolute_error: 3.3563 - mean_squared_error: 25.8886\n",
      "Testing set Mean Abs Error:  3.36 thousand dollars\n"
     ]
    }
   ],
   "source": [
    "loss, mae, mse = model.evaluate(normed_test_data, test_targets, verbose=2)\n",
    "\n",
    "print(\"Testing set Mean Abs Error: {:5.2f} thousand dollars\".format(mae))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 绘制预测误差图\n",
    "\n",
    "横坐标为真实值，纵坐标为预测值，绘制的点离baseline越近越好"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-20T17:17:33.728480Z",
     "start_time": "2020-04-20T17:17:33.536217Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f6b32af6a10>"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_predictions = model.predict(normed_test_data).flatten()\n",
    "\n",
    "plt.scatter(test_targets, test_predictions)\n",
    "plt.xlabel('True Values [mdev]')\n",
    "plt.ylabel('Predictions [mdev]')\n",
    "plt.axis('equal')\n",
    "plt.axis('square')\n",
    "plt.xlim([0, plt.xlim()[1]])  # 取实际上限，下限置0较为美观\n",
    "plt.ylim([0, plt.ylim()[1]])\n",
    "plt.plot([-100, 100], [-100, 100], label=\"baseline\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 查看误差分布\n",
    "\n",
    "可见总体误差较小"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-20T17:21:19.811825Z",
     "start_time": "2020-04-20T17:21:19.601090Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "error = test_predictions - test_targets  # 由于test_labels带序号，故error带序号\n",
    "# print(error)\n",
    "plt.hist(error, bins=25)  # bins为hist宽度\n",
    "plt.xlabel(\"Prediction Error [mdev]\")\n",
    "plt.ylabel(\"Count\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 计算数据相关性\n",
    "\n",
    "[参考1：sklearn中的特征提取](http://d0evi1.com/sklearn/feature_selection/)\n",
    "\n",
    "[参考2：如何从sklearn.feature_selection.SelectKBest获得每个功能的分数？](https://stackoom.com/question/2DDBx/%E5%A6%82%E4%BD%95%E4%BB%8Esklearn-feature-selection-SelectKBest%E8%8E%B7%E5%BE%97%E6%AF%8F%E4%B8%AA%E5%8A%9F%E8%83%BD%E7%9A%84%E5%88%86%E6%95%B0)\n",
    "\n",
    "### 导包"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-20T17:28:45.641706Z",
     "start_time": "2020-04-20T17:28:45.637665Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(404, 13)"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.datasets import load_digits\n",
    "from sklearn.feature_selection import SelectKBest, f_regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "使用sklearn库的SelectKBest功能，为回归问题中的每个参数打分"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-20T18:06:21.659375Z",
     "start_time": "2020-04-20T18:06:21.653372Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 67.2207808 ,  67.97051963, 118.24290924,  11.7702596 ,\n",
       "        95.60584689, 348.58593894,  61.46573323,  27.70084155,\n",
       "        65.99230268, 101.35861781, 129.76412369,  53.93942132,\n",
       "       460.76844365])"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "selector = SelectKBest(f_regression, k=2).fit(train_data, train_targets)\n",
    "x_new = selector.transform(train_data) # not needed to get the score\n",
    "scores = selector.scores_\n",
    "scores"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "将分数降序排列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-20T18:09:53.671478Z",
     "start_time": "2020-04-20T18:09:53.667673Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([12,  5, 10,  2,  9,  4,  1,  0,  8,  6, 11,  7,  3])"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_values = np.argsort(-scores)\n",
    "feature_values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "共14个输入特征，分别为：\n",
    "\n",
    "crim:犯罪率、zn:城镇住宅用地占比、indus:城镇非商业用地占比、chas：查尔斯指数\n",
    "\n",
    "nox：环保指标、rm：房间数、age：房龄比例、dis：与就业中心的距离\n",
    "\n",
    "rad：高度路便利指数、tax：税率、ptratio：城镇师生比、black：黑人比例\n",
    "\n",
    "lstat：房东是收入阶层的比例、medv：房价中位数(目标，train_targets)\n",
    "\n",
    "将姓名列表转化为np数组，方便排序\n",
    "\n",
    "原始排列如下："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-20T17:57:59.614239Z",
     "start_time": "2020-04-20T17:57:59.610606Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['crim', 'zn', 'indus', 'chas', 'nox', 'rm', 'age', 'dis', 'rad',\n",
       "       'tax', 'ptratio', 'black', 'lstat'], dtype='<U7')"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np_column_names = np.array(column_names)\n",
    "np_column_names"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从大到小排列后，数据如下\n",
    "\n",
    "**可看出：房东是收入阶层的比例、房间数与城镇师生比 是最主要的三个影响房价因素**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-20T17:46:39.662063Z",
     "start_time": "2020-04-20T17:46:39.658287Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['lstat', 'rm', 'ptratio', 'indus', 'tax', 'nox', 'zn', 'crim',\n",
       "       'rad', 'age', 'black', 'dis', 'chas'], dtype='<U7')"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array(column_names)[feature_values]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-20T18:09:33.510947Z",
     "start_time": "2020-04-20T18:09:33.335032Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 13 artists>"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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7Pj7c1vu8JI9djfZe3+q7JMlx7RPlv7Tn+mMZ+qTb5h3R7vPaJC9I8oW27sfM8DEavCaPTrK0PV4fHFpu5HM+TdujtpeDk/yiveaOGlqHRUlObM/BBUmeM6LJhRO2gUdOuL8jJql91yQ/bo/lvyfZbMLt9k3ykyTbTLIe70vXSef36HpQeNAn/CSHJflZq+sT0zwmD3pe2+Tnt/quHWpz00m2ham2pbWnqublH3Bn+/93wPva8AJgs+H5Q8tv1f5vAlwObD1quSnubzFwP7B7Gy/gJW34JOAMYAPgWcDFE25XwHPa+BeAdwLLgG2GlivgVRPrbcPHAS9rw+cAS4bmnUMXFI9r9S0C9gZWAgfTfXD4CfBcYGPgemAnuivQTwBObe18AHjnULuXt9r/C3DU0PQtJqzb5W14IbB5G94GuIYHvh33j8An6DpMfM9qPN4rgZ3b+AnAXwKXAi9o0z4E/GsbfjpwJfDnwE+BDdfgNbUB8APgZe35GDzm/wT8/QzbeDpd1y3bDJ5H4BjgVGBBm/YG4NNt+Bi6fsJC11fYHcAz2vN24WD9Z/iaHLzGF7TXxTOnes6nWY+J28t2dK/ZrYYep8E6fBl4bhv+I+DKEXWO2gbOob2WJ6l9Q+BaYNc2b/P2OnsD8Gngla2OR0+yDs8GLqPbI9+c7jX5zvaY79/W5SoeeJ1uuQbP69fbc/U0ur7fYJJtgSm2pbX5N+/3FOgumntjkg8Az6iq306y3NuTXAKcR3fl9U5rcF+/qqrz2vA9wHfa8GXA96vq3ja8eMLtrq+qH7XhL9G9QU90H3Di0PgLk5yf5DJgL7oX5VR2Be6rqhWtrauAp1fV/cDFraanANdV1dXVvSq/NE2bg3XbJ91e0fOq6vZJlgvwkSSXAt+jexMZfLr+EN2b9RK6N9iZuq6qLm7DFwJPoNtwv9+mHQs8H6C6a2KOA74FvKm6ThlX1yeBs6rqW3TP7+DY+4Ws+pxOZi/gG1V1c6vrljb961V13yS3+VZ7Pi4DflNVl7Xn7YoZ3O/wa/JVSS6iC8Wn071RrclzDqtuL6+je43f0l7nXx9adh/g00kuprtgdfOJn+iZfhsYVfuTgRur6gKAqrqjqla25V8IvBvYt6punWQdngecVFV3VdUdrHox7R3A3cDnk/wFcNcUj8dkz+s3q+r+6g7hDl7vk20LM92WHpJ5HwpVdS7dG8N/AMclef3EZdphhX2AParqWXQvvI3X4O5+NzR8b9vIoPu09odWz/2selHhxItJRl1ccvfgTSPJxsBngf2r6hnAUTOod2LfUyuHhu8bqmmyC1tW8uDX08YAVfULHvjE9dEk75/k9gfS7aU8u6p2Bn4zVPNWwKbAZjNYj2F/mLAOW06z/DOA23hg45yxJG8AHg8MDl0MP7/Dj9+0TTH6Mf7diGkDg/W8nwev86jX0sh2k+xI9yl476p6JnAaDzzWq3Ux0yTby1QdVz6iLbtz+9tuxIezSbeBKWqf7LGEbg9iM+BJ06zOpOveAmY3ug9jr+CBD3mjTFbLHyYsA5NsC6uxLT0k8z4UkjweuKmqjgKOBnZps+5NskEb3gK4taruSvIUYPehJoaXG5c/SrJHG34t8EPgt3Qv6lEGG/PNSTal29UdmOx25wML2rHVR9AdTvr+hGV+DuyY5AlDtQwsoz12SXYBdmzDjwPuqqov0R0C2mXoNsO1bEH3PNyb5IV0b7ADRwL/Czge+Ngk6zwTtwO3JnleG38dbR3bJ72t6T4gfCrJdAHSS/Jsujelv2yh/lCcSfepd+vW9lYPsb2Z2pwuIG5Pd/7jJW36VM/5ZEZtL48EXpB2DofuUMjAGUDfG3KSnUe0OWobmEntj0uya2t3s3bfAL+iOz/yxSST7UWfC7wy3bmozegOC/batrVFVX2b7osfo+oeWJ3ndeS2MM22tNasM91czKE9gXcluRe4ExjsKRwJXNp2Sd8EvLntzl1Ft0vMxOWq6sAx1XglcFCSzwFXA0fQHZ74tyQ3VtULhxeuqtuSHEX3iWIZ3SGygWOA/5Pk98AeQ7e5Mck9wNl0n8pvr6qTJ7R7d7qThqcluZluwxyc9D0ReH07BHAB8Is2/RnAx5PcD9wLvGWovf+X5Efpvm54AfCUJEvpDlf9HLqTc8DKqvpyum9V/TjJXlV11mo+hgMHtfV/JN2nxTe2IDyM7pPm9elOgH6yLTsTb6Pbmzk73fn4pWtYG1V1RbqT3N9Pch/dp+yxq6pLkvyU7pDTtcCP2vSpnvPJfIdVt5f/AD5C9+HjBuBndCEN8HbgM235hXRvxm+e0OaobeBl09R+TzsZ+7+TbAL8nm4PZrDOVyU5EPh6kpdV1S8nPCYXJfka3evxV3TnH4ZtBpzc9swD/I/JHpDVfF6PB741cVtgim1pbbKbi3VcksV0J/am/caNtC5LsmlV3dk+rZ9E17/ZSXNdlx5s3h8+kjRrPtD2JC8HrgO+Ocf1aAT3FCRJPfcUJEk9Q0GS1DMUJEk9Q0EPK0nuywP9EF3cvr21um2MtffWdH0X3Z/kmUPTLl+TWqW1zVDQw83vh66M3bmqlq1BG2vUe2um7p12ouXA+1b3PqRxMxT0sJdkQZKPp+uB89Ik/61NH9kbJQ/uvfXjmfAbAkk+3bq1IMmydD3S/hA4IMkTknwnXQ+hP2hX9I5yKvD0JE8eUe9kPX4uS/KRdL16Lk2yS5LTk/wyyZuHlnvX0Lp+cGL70lS8olkPN5u078JD15HbK+l6e729qnZNshHwoyRn0PX++cqquqNd1XxeklOAQ4E/bv3ODPrymcrdVfXctuyZwJur6uokf0rXB9VeI25zP13nfu9l1Sun31dVt7Q9jzOTPLOqLm3zrq+qPZIcTnd1+nPoujW5gu5K7RfRdda4G91VtqckeX7r40ualqGgh5vfD97Mh7wIeGYe+JWrLejeOJfT9Ub5fLo36eGeWVfH16DvC+fP6LpNGMzbaIrbfRl4X7oO3Ya9qnUtsRDYlq7Hz0EoDHrqvAzYtHUc99t0v4WxZVvXF/FANwqbtnU1FDQjhoLmgwB/XVWnP2hidwho0BvlvUmWMboX1pE9wA4Z9GD6COC2EaE0UlWtTPLPdF04D2oa9Pi5a1Xdmu6Hcobvb7oeUQN8tKo+N5MapIk8p6D54HTgLWm92SZ5UpJHMXnPrBN7kv0V8LQkGyXZgu5HiFbR+ty/LskB7X6S5FnT1HYMXSdti9r4ZD1+ztTpwJvaXgtJtkvymNVsQ/OYewqaDz5P92MzF6U7rrOCrv/7kb1RTui99d+q6l1JTqA7hHM1U/dweSBwRJK/p/uFsa8Cl0y2cOvJ81N0vbJO2uPnTFXVGUmeCvykHcK6k+4X525anXY0f9n3kSSp5+EjSVLPUJAk9QwFSVLPUJAk9QwFSVLPUJAk9QwFSVLv/wN4DIttNsXIwwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.ylabel('Score')\n",
    "plt.xlabel('Feature Name')\n",
    "plt.bar(np.array(column_names)[feature_values],scores[feature_values])"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
